OpenRaiser/Intern-Atlas huggingface dataset:
VPS: Excavating High-Level C++ Constructs from Low-Level Binaries to Protect Dynamic Dispatching
Venue: arXiv cs.CR 2020
Year: 2020

Polymorphism and inheritance make C++ suitable for writing complex software, but significantly increase the attack surface because the implementation relies on virtual function tables (vtables). These vtables contain function pointers that attackers can potentially hijack and in practice, vtable hijacking is one of the most important attack vector for C++ binaries. In this paper, we present VTable Pointer Separation (VPS), a practical binary-level defense against vtable hijacking in C++ applications. Unlike previous binary-level defenses, which rely on unsound static analyses to match classes to virtual callsites, VPS achieves a more accurate protection by restricting virtual callsites to validly created objects. More specifically, VPS ensures that virtual callsites can only use objects created at valid object construction sites, and only if those objects can reach the callsite. Moreover, VPS explicitly prevents false positives (falsely identified virtual callsites) from breaking the binary, an issue existing work does not handle correctly or at all. We evaluate the prototype implementation of VPS on a diverse set of complex, real-world applications (MongoDB, MySQL server, Node.js, SPEC CPU2017/CPU2006), showing that our approach protects on average 97.8% of all virtual callsites in SPEC CPU2006 and 97.4% in SPEC CPU2017 (all C++ benchmarks), with a moderate performance overhead of 11% and 9% geomean, respectively. Furthermore, our evaluation reveals 86 false negatives in VTV, a popular source-based defense which is part of GCC.
Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach
Venue: arXiv cs.LG 2020
Year: 2020

Failure of mission-critical equipment interrupts production and results in monetary loss. The risk of unplanned equipment downtime can be minimized through Predictive Maintenance of revenue generating assets to ensure optimal performance and safe operation of equipment. However, the increased sensorization of the equipment generates a data deluge, and existing machine-learning based predictive model alone becomes inadequate for timely equipment condition predictions. In this paper, a model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equipment-based sensor network context. Within each equipment, a sensor device aggregates raw sensor data, and the equipment health status is analyzed for anomalous events. Unlike traditional black-box regression models, the proposed algorithm self-learns an optimal maintenance policy and provides actionable recommendation for each equipment. Our experimental results demonstrate the potential for broader range of equipment maintenance applications as an automatic learning framework.
Safety Controller Synthesis for Collaborative Robots
Venue: arXiv cs.RO 2020
Year: 2020

In human-robot collaboration (HRC), software-based automatic safety controllers (ASCs) are used in various forms (e.g. shutdown mechanisms, emergency brakes, interlocks) to improve operational safety. Complex robotic tasks and increasingly close human-robot interaction pose new challenges to ASC developers and certification authorities. Key among these challenges is the need to assure the correctness of ASCs under reasonably weak assumptions. To address this need, we introduce and evaluate a tool-supported ASC synthesis method for HRC in manufacturing. Our ASC synthesis is: (i) informed by the manufacturing process, risk analysis, and regulations; (ii) formally verified against correctness criteria; and (iii) selected from a design space of feasible controllers according to a set of optimality criteria. The synthesised ASC can detect the occurrence of hazards, move the process into a safe state, and, in certain circumstances, return the process to an operational state from which it can resume its original task.
C2G-Net: Exploiting Morphological Properties for Image Classification
Venue: arXiv cs.CV 2020
Year: 2020

In this paper we propose C2G-Net, a pipeline for image classification that exploits the morphological properties of images containing a large number of similar objects like biological cells. C2G-Net consists of two components: (1) Cell2Grid, an image compression algorithm that identifies objects using segmentation and arranges them on a grid, and (2) DeepLNiNo, a CNN architecture with less than 10,000 trainable parameters aimed at facilitating model interpretability. To test the performance of C2G-Net we used multiplex immunohistochemistry images for predicting relapse risk in colon cancer. Compared to conventional CNN architectures trained on raw images, C2G-Net achieved similar prediction accuracy while training time was reduced by 85% and its model was is easier to interpret.
Optical Navigation in Unstructured Dynamic Railroad Environments
Venue: arXiv cs.RO 2020
Year: 2020

We present an approach for optical navigation in unstructured, dynamic railroad environments. We propose a way how to cope with the estimation of the train motion from sole observations of the planar track bed. The occasional significant occlusions during the operation of the train limit the available observation to this difficult to track, repetitive area. This approach is a step towards replacement of the expensive train management infrastructure with local intelligence on the train for SmartRail 4.0. We derive our approach for robust estimation of translation and rotation in this difficult environments and provide experimental validation of the approach on real rail scenarios.
Group Equivariant Deep Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

In Reinforcement Learning (RL), Convolutional Neural Networks(CNNs) have been successfully applied as function approximators in Deep Q-Learning algorithms, which seek to learn action-value functions and policies in various environments. However, to date, there has been little work on the learning of symmetry-transformation equivariant representations of the input environment state. In this paper, we propose the use of Equivariant CNNs to train RL agents and study their inductive bias for transformation equivariant Q-value approximation. We demonstrate that equivariant architectures can dramatically enhance the performance and sample efficiency of RL agents in a highly symmetric environment while requiring fewer parameters. Additionally, we show that they are robust to changes in the environment caused by affine transformations.
Characterization and comparison of large directed graphs through the spectra of the magnetic Laplacian
Venue: arXiv cs.SI 2020
Year: 2020

In this paper we investigated the possibility to use the magnetic Laplacian to characterize directed graphs (a.k.a. networks). Many interesting results are obtained, including the finding that community structure is related to rotational symmetry in the spectral measurements for a type of stochastic block model. Due the hermiticity property of the magnetic Laplacian we show here how to scale our approach to larger networks containing hundreds of thousands of nodes using the Kernel Polynomial Method (KPM). We also propose to combine the KPM with the Wasserstein metric in order to measure distances between networks even when these networks are directed, large and have different sizes, a hard problem which cannot be tackled by previous methods presented in the literature. In addition, our python package is publicly available at \href{https://github.com/stdogpkg/emate}{github.com/stdogpkg/emate}. The codes can run in both CPU and GPU and can estimate the spectral density and related trace functions, such as entropy and Estrada index, even in directed or undirected networks with million of nodes.
Composite Metrics for Network Security Analysis
Venue: arXiv cs.CR 2020
Year: 2020

Security metrics present the security level of a system or a network in both qualitative and quantitative ways. In general, security metrics are used to assess the security level of a system and to achieve security goals. There are a lot of security metrics for security analysis, but there is no systematic classification of security metrics that are based on network reachability information. To address this, we propose a systematic classification of existing security metrics based on network reachability information. Mainly, we classify the security metrics into host-based and network-based metrics. The host-based metrics are classified into metrics ``without probability" and "with probability", while the network-based metrics are classified into "path-based" and "non-path based". Finally, we present and describe an approach to develop composite security metrics and it's calculations using a Hierarchical Attack Representation Model (HARM) via an example network. Our novel classification of security metrics provides a new methodology to assess the security of a system.
Economically Viable Randomness
Venue: arXiv cs.CR 2020
Year: 2020

We study the problem of providing blockchain applications with \emph{economically viable randomness} (EVR), namely, randomness that has significant economic consequences. Applications of EVR include blockchain-based lotteries and gambling. An EVR source guarantees (i) secrecy, assuring that the random bits are kept secret until some predefined condition indicates that they are safe to reveal (e.g., the lottery's ticket sale closes), and (ii) robustness, guaranteeing that the random bits are published once the condition holds. We formalize the EVR problem and solve it on top of an Ethereum-like blockchain abstraction, which supports smart contracts and a transferable native coin. Randomness is generated via a distributed open commit-reveal scheme by game-theoretic agents who strive to maximize their coin holdings. Note that in an economic setting, such agents might profit from breaking secrecy or robustness, and may engage in side agreements (via smart contracts) to this end. Our solution creates an incentive structure that counters such attacks. We prove that following the protocol gives rise to a stable state, called Coalition-Proof Nash Equilibrium, from which no coalition comprised of a subset of the players can agree to deviate. In this stable state, robustness and secrecy are satisfied. Finally, we implement our EVR source over Ethereum.
Meta Corrupted Pixels Mining for Medical Image Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Deep neural networks have achieved satisfactory performance in piles of medical image analysis tasks. However the training of deep neural network requires a large amount of samples with high-quality annotations. In medical image segmentation, it is very laborious and expensive to acquire precise pixel-level annotations. Aiming at training deep segmentation models on datasets with probably corrupted annotations, we propose a novel Meta Corrupted Pixels Mining (MCPM) method based on a simple meta mask network. Our method is targeted at automatically estimate a weighting map to evaluate the importance of every pixel in the learning of segmentation network. The meta mask network which regards the loss value map of the predicted segmentation results as input, is capable of identifying out corrupted layers and allocating small weights to them. An alternative algorithm is adopted to train the segmentation network and the meta mask network, simultaneously. Extensive experimental results on LIDC-IDRI and LiTS datasets show that our method outperforms state-of-the-art approaches which are devised for coping with corrupted annotations.
Breaking and Fixing Destructive Code Read Defenses
Venue: arXiv cs.CR 2020
Year: 2020

Just-in-time return-oriented programming (JIT-ROP) is a powerful memory corruption attack that bypasses various forms of code randomization. Execute-only memory (XOM) can potentially prevent these attacks, but requires source code. In contrast, destructive code reads (DCR) provide a trade-off between security and legacy compatibility. The common belief is that DCR provides strong protection if combined with a high-entropy code randomization. The contribution of this paper is twofold: first, we demonstrate that DCR can be bypassed regardless of the underlying code randomization scheme. To this end, we show novel, generic attacks that infer the code layout for highly randomized program code. Second, we present the design and implementation of BGDX (Byte-Granular DCR and XOM), a novel mitigation technique that protects legacy binaries against code inference attacks. BGDX enforces memory permissions on a byte-granular level allowing us to combine DCR and XOM for legacy, off-the-shelf binaries. Our evaluation shows that BGDX is not only effective, but highly efficient, imposing only a geometric mean performance overhead of 3.95% on SPEC.
Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction
Venue: arXiv cs.LG 2020
Year: 2020

The rapid growth and distribution of IT systems increases their complexity and aggravates operation and maintenance. To sustain control over large sets of hosts and the connecting networks, monitoring solutions are employed and constantly enhanced. They collect diverse key performance indicators (KPIs) (e.g. CPU utilization, allocated memory, etc.) and provide detailed information about the system state. Storing such metrics over a period of time naturally raises the motivation of predicting future KPI progress based on past observations. Although, a variety of time series forecasting methods exist, forecasting the progress of IT system KPIs is very hard. First, KPI types like CPU utilization or allocated memory are very different and hard to be expressed by the same model. Second, system components are interconnected and constantly changing due to soft- or firmware updates and hardware modernization. Thus a frequent model retraining or fine-tuning must be expected. Therefore, we propose a lightweight solution for KPI series prediction based on historic observations. It consists of a weighted heterogeneous ensemble method composed of two models - a neural network and a mean predictor. As ensemble method a weighted summation is used, whereby a heuristic is employed to set the weights. The modelling approach is evaluated on the available FedCSIS 2020 challenge dataset and achieves an overall $R^2$ score of 0.10 on the preliminary 10% test data and 0.15 on the complete test data. We publish our code on the following github repository: https://github.com/citlab/fed_challenge
A Decade of Social Bot Detection
Venue: arXiv cs.CY 2020
Year: 2020

On the morning of November 9th 2016, the world woke up to the shocking outcome of the US Presidential elections: Donald Trump was the 45th President of the United States of America. An unexpected event that still has tremendous consequences all over the world. Today, we know that a minority of social bots, automated social media accounts mimicking humans, played a central role in spreading divisive messages and disinformation, possibly contributing to Trump's victory. In the aftermath of the 2016 US elections, the world started to realize the gravity of widespread deception in social media. Following Trump's exploit, we witnessed to the emergence of a strident dissonance between the multitude of efforts for detecting and removing bots, and the increasing effects that these malicious actors seem to have on our societies. This paradox opens a burning question: What strategies should we enforce in order to stop this social bot pandemic? In these times, during the run-up to the 2020 US elections, the question appears as more crucial than ever. What stroke social, political and economic analysts after 2016, deception and automation, has been however a matter of study for computer scientists since at least 2010. In this work, we briefly survey the first decade of research in social bot detection. Via a longitudinal analysis, we discuss the main trends of research in the fight against bots, the major results that were achieved, and the factors that make this never-ending battle so challenging. Capitalizing on lessons learned from our extensive analysis, we suggest possible innovations that could give us the upper hand against deception and manipulation. Studying a decade of endeavours at social bot detection can also inform strategies for detecting and mitigating the effects of other, more recent, forms of online deception, such as strategic information operations and political trolls.
PAN IIT Survey on Online Education: A Report
Venue: arXiv cs.CY 2020
Year: 2020

Due to the COVID-19 pandemic that set in in the middle of the semester, most of the IITs (and other institutions from across India and the world) switched to an online mode for completing the semester. Since then, many different institutions from across the world have been discussing ways and means to make online education effective, via various means, such as webinars, talks and short workshops. However, a general observation has been that there is a lack of data on online education, particularly in the Indian context. Anticipating this, we decided to conduct a pan-IIT survey on online education and compiled two sets of questionnaires (one each for faculty members and students) in discussion with the members of the pan-IIT group on Online Pedagogy (https://www.paniit-onlinepedagogy-researchgroup.net/), that has been formed for conducting research on the broad area of online education. The current survey has been steered by IIT Dharwad and IIT Jammu along with the members of the pan-IIT group on online pedagogy. The survey data was collected from 5th May 2020 to 25th May 2020. We received about 11,890 and 840 responses from students and faculty members, respectively. About 82% and 86% of the respondents were males among the students and faculty surveys, respectively. In the following, we present and discuss the survey responses pertaining to various aspects of online education. An infographic representation of the survey responses may be found in Appendix. We note that the survey conducted was a broad survey, covering various broad aspects of online education. However, a fine-grained survey may be required to obtain an in-depth understanding of specific aspects, such as on how to handle lab courses and carry out assessments.
Artificial Stupidity
Venue: arXiv cs.CY 2020
Year: 2020

Public debate about AI is dominated by Frankenstein Syndrome, the fear that AI will become superhuman and escape human control. Although superintelligence is certainly a possibility, the interest it excites can distract the public from a more imminent concern: the rise of Artificial Stupidity (AS). This article discusses the roots of Frankenstein Syndrome in Mary Shelley's famous novel of 1818. It then provides a philosophical framework for analysing the stupidity of artificial agents, demonstrating that modern intelligent systems can be seen to suffer from 'stupidity of judgement'. Finally it identifies an alternative literary tradition that exposes the perils and benefits of AS. In the writings of Edmund Spenser, Jonathan Swift and E.T.A. Hoffmann, ASs replace, oppress or seduce their human users. More optimistically, Joseph Furphy and Laurence Sterne imagine ASs that can serve human intellect as maps or as pipes. These writers provide a strong counternarrative to the myths that currently drive the AI debate. They identify ways in which even stupid artificial agents can evade human control, for instance by appealing to stereotypes or distancing us from reality. And they underscore the continuing importance of the literary imagination in an increasingly automated society.
The CMS monitoring infrastructure and applications
Venue: arXiv cs.SE 2020
Year: 2020

The globally distributed computing infrastructure required to cope with the multi-petabytes datasets produced by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) at CERN comprises several subsystems, such as workload management, data management, data transfers, and submission of users' and centrally managed production requests. The performance and status of all subsystems must be constantly monitored to guarantee the efficient operation of the whole infrastructure. Moreover, key metrics need to be tracked to evaluate and study the system performance over time. The CMS monitoring architecture allows both real-time and historical monitoring of a variety of data sources and is based on scalable and open source solutions tailored to satisfy the experiment's monitoring needs. We present the monitoring data flow and software architecture for the CMS distributed computing applications. We discuss the challenges, components, current achievements, and future developments of the CMS monitoring infrastructure.
Streaming Complexity of SVMs
Venue: arXiv cs.DS 2020
Year: 2020

We study the space complexity of solving the bias-regularized SVM problem in the streaming model. This is a classic supervised learning problem that has drawn lots of attention, including for developing fast algorithms for solving the problem approximately. One of the most widely used algorithms for approximately optimizing the SVM objective is Stochastic Gradient Descent (SGD), which requires only $O(\frac{1}{λε})$ random samples, and which immediately yields a streaming algorithm that uses $O(\frac{d}{λε})$ space. For related problems, better streaming algorithms are only known for smooth functions, unlike the SVM objective that we focus on in this work. We initiate an investigation of the space complexity for both finding an approximate optimum of this objective, and for the related ``point estimation'' problem of sketching the data set to evaluate the function value $F_λ$ on any query $(θ, b)$. We show that, for both problems, for dimensions $d=1,2$, one can obtain streaming algorithms with space polynomially smaller than $\frac{1}{λε}$, which is the complexity of SGD for strongly convex functions like the bias-regularized SVM, and which is known to be tight in general, even for $d=1$. We also prove polynomial lower bounds for both point estimation and optimization. In particular, for point estimation we obtain a tight bound of $Θ(1/\sqrtε)$ for $d=1$ and a nearly tight lower bound of $\widetildeΩ(d/ε^2)$ for $d = Ω( \log(1/ε))$. Finally, for optimization, we prove a $Ω(1/\sqrtε)$ lower bound for $d = Ω( \log(1/ε))$, and show similar bounds when $d$ is constant.
Promoting Strategic Research on Inclusive Access to Rich Online Content and Services
Venue: arXiv cs.CY 2020
Year: 2020

Access to content and services online is increasingly important for everyone, including people with disabilities. National commitments, including the Americans with Disabilities Act, and international resolutions, including the United Nations Declaration of the Rights of Persons with Disabilities, call for work to ensure that people with disabilities can participate fully in the online world. Gains in education, employment and health, as well as in civic engagement, social participation, and personal independence will follow from enhanced inclusion online. Research in many areas of computer science, including recognition technology, natural language processing, personalization, software architecture, and others, is needed to secure these benefits. Organizing this research calls for partnerships among academic researchers, federal agencies, and commercial organizations, as well as effective division of labor and cooperation between computer scientists, behavioral scientists, advocacy groups, and consumers.